Tongji-GRACE2022: New global temporal earth’s gravity field models derived from k-band and lri inter-satellite rang-rate data
Abstract: In order to analyze the influence of K-band and LRI inter-satellite range-rate observation from GRACE-FO on gravity field recovery. Incorporating both K-Band and LRI inter-satellite range-rate data from GRACE-FO into gravity field recovery, we derive a new time series of monthly solutions...
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ftgfzpotsdam:oai:gfzpublic.gfz-potsdam.de:item_5016601 2023-08-20T04:06:57+02:00 Tongji-GRACE2022: New global temporal earth’s gravity field models derived from k-band and lri inter-satellite rang-rate data Qiujie, C. Shen, Z. Yunzhong, S. Wu, C. Xingfu, Z. 2023 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5016601 eng eng info:eu-repo/semantics/altIdentifier/doi/10.57757/IUGG23-0878 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5016601 XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) info:eu-repo/semantics/conferenceObject 2023 ftgfzpotsdam https://doi.org/10.57757/IUGG23-0878 2023-07-30T23:40:44Z Abstract: In order to analyze the influence of K-band and LRI inter-satellite range-rate observation from GRACE-FO on gravity field recovery. Incorporating both K-Band and LRI inter-satellite range-rate data from GRACE-FO into gravity field recovery, we derive a new time series of monthly solutions entitled Tongji-Grace2022 complete to degrees and orders 60, 96, and 120 over the period Apr. 2002 to Sept.2022. During deriving Tongji-Grace2022, the[S1] low-degree coefficients up to d/o 6 were co-estimated with the high-degree coefficients to account for the temporal variations within one month. Analyses of Tongji-Grace2022 allow us to draw the following conclusions: (1) In the time domain, the RMS of LRI post-fit residuals is about 50% less than that of KBR; (2) In the frequency domain, the LRI shows significant noise reduction as compared to K-band data at high frequencies; (2) The comparison of Tongji-Grace2022 to CSR RL06, JPL RL06, GFZ RL06, and ITSG‐Grace2018 in terms of geoid degree variances suggests that Tongji-Grace2022 agrees well with other GRACE models at the low degrees (below degree 30), while the high-frequency noise in Tongji-Grace2022 is significantly reduced; (3) Less striping noise over oceans can be observed in Tongji-Grace2022 even only using decorrelation filtering (P4M6); (4) Over the selected river basins (i.e., Amazon, Mississippi, and Ganges) and Greenland, the correlation coefficient of the mass changes between Tongji-Grace2022 and others (i.e., CSR RL06, JFZ RL06, ITSG‐ Grace2018) are all over 92%. Conference Object Greenland GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam) Greenland |
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GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam) |
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ftgfzpotsdam |
language |
English |
description |
Abstract: In order to analyze the influence of K-band and LRI inter-satellite range-rate observation from GRACE-FO on gravity field recovery. Incorporating both K-Band and LRI inter-satellite range-rate data from GRACE-FO into gravity field recovery, we derive a new time series of monthly solutions entitled Tongji-Grace2022 complete to degrees and orders 60, 96, and 120 over the period Apr. 2002 to Sept.2022. During deriving Tongji-Grace2022, the[S1] low-degree coefficients up to d/o 6 were co-estimated with the high-degree coefficients to account for the temporal variations within one month. Analyses of Tongji-Grace2022 allow us to draw the following conclusions: (1) In the time domain, the RMS of LRI post-fit residuals is about 50% less than that of KBR; (2) In the frequency domain, the LRI shows significant noise reduction as compared to K-band data at high frequencies; (2) The comparison of Tongji-Grace2022 to CSR RL06, JPL RL06, GFZ RL06, and ITSG‐Grace2018 in terms of geoid degree variances suggests that Tongji-Grace2022 agrees well with other GRACE models at the low degrees (below degree 30), while the high-frequency noise in Tongji-Grace2022 is significantly reduced; (3) Less striping noise over oceans can be observed in Tongji-Grace2022 even only using decorrelation filtering (P4M6); (4) Over the selected river basins (i.e., Amazon, Mississippi, and Ganges) and Greenland, the correlation coefficient of the mass changes between Tongji-Grace2022 and others (i.e., CSR RL06, JFZ RL06, ITSG‐ Grace2018) are all over 92%. |
format |
Conference Object |
author |
Qiujie, C. Shen, Z. Yunzhong, S. Wu, C. Xingfu, Z. |
spellingShingle |
Qiujie, C. Shen, Z. Yunzhong, S. Wu, C. Xingfu, Z. Tongji-GRACE2022: New global temporal earth’s gravity field models derived from k-band and lri inter-satellite rang-rate data |
author_facet |
Qiujie, C. Shen, Z. Yunzhong, S. Wu, C. Xingfu, Z. |
author_sort |
Qiujie, C. |
title |
Tongji-GRACE2022: New global temporal earth’s gravity field models derived from k-band and lri inter-satellite rang-rate data |
title_short |
Tongji-GRACE2022: New global temporal earth’s gravity field models derived from k-band and lri inter-satellite rang-rate data |
title_full |
Tongji-GRACE2022: New global temporal earth’s gravity field models derived from k-band and lri inter-satellite rang-rate data |
title_fullStr |
Tongji-GRACE2022: New global temporal earth’s gravity field models derived from k-band and lri inter-satellite rang-rate data |
title_full_unstemmed |
Tongji-GRACE2022: New global temporal earth’s gravity field models derived from k-band and lri inter-satellite rang-rate data |
title_sort |
tongji-grace2022: new global temporal earth’s gravity field models derived from k-band and lri inter-satellite rang-rate data |
publishDate |
2023 |
url |
https://gfzpublic.gfz-potsdam.de/pubman/item/item_5016601 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland |
genre_facet |
Greenland |
op_source |
XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.57757/IUGG23-0878 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5016601 |
op_doi |
https://doi.org/10.57757/IUGG23-0878 |
_version_ |
1774718336396951552 |